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News Analysis: Celonis Launches Execution Management System

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Eliminating System Complexity Is Celonis’ Number One Priority

On October 14th, 2020, Co-CEO and Co-Founder, Alexander Rinke, kicked off its Ecosystem Summit and showcased its new Execution Management System (EMS).  The goal is to improve an enterprise’s execution capacity by simplifying complex processes.  Celonis defines execution capacity as the ability to get things done over the time + cost + effort (see Figure 1).

Figure 1. Celonis Defines Execution CapacitySource: Celonis

Celonis uses its process mining core to deliver the Celonis EMS Platform.  The Bavarian software startup is headquartered in Munich, Germany and New York City, USA with 15 offices worldwide.

The Celonis Execution Management System

The Celonis Execution Management System (EMS) brings together a number of applications, instruments, development studios, tools and platform approaches to unlock execution capacity.  Sitting on top of business processes and systems, the heart of the solution is the process mining core that identifies and measures capacity barriers.  Capacity is unlocked by a methodic approach to applying best practices, actions, and automation.  The new additions and updates from the fall launch of EMS include:

  • Celonis Execution Applications.  These operational applications include popular apps such as Celonis Accounts Payable, Accounts Receivable, and Opportunity Management.
  • Celonis Execution Instruments. What was formerly known as process mining analytics applications, can be used to measure current execution capacity and identify execution gaps.  Over 170 instruments have been developed to date.
  • Celonis Studio.  Ecosystem partners and customers use the studio to build new instruments and applications.  
  • Celonis EMS Store.  New solutions can be ecosystem partners can be found in the store

Figure 2. Inside The Celonis EMS Platform

Source: Celonis

Proof Is In The Pudding

At the Ecosystem Summit, Celonis showcased a number of use cases where EMS improved operations.  For example, AVNET reduced throughput times order management by 50% from order to ship.  Dell was able to move to a leaner supply chain with 8 days of inventory versus an industry average of 40 days.  Ascend improved on time delivery by 27% while simultaneously increasing automation by 43% in just four months.  L’Oreal was able to grow on-time touchless orders by 800%, resulting in greater revenue and capacity to ship more products using existing infrastructure.  Comcast improved asset utilization and captured $85 million in improvements.

Overall, this ability to improve execution capacity has created more capacity with existing infrastructure.  In supply chains, the average on-time delivery rate is 43%, yet Celonis customers using EMS can hit a 95% target.  Celonis clams that the average touchless invoice rate is only 27% inside companies while Celonis best-in-class customers are exceeding 90% adoption for finance and administration use cases.  In customer experience, most companies average a 32 net promoter score (NPS), yet Celonis best-in-class customers appear to double scores at an average of 70 NPS.

Celonis customers include venerable brands such as ABB, AstraZeneca, Bosch, Coca Cola, Citibank, Dell, GSK, John Deere, L’Oreal, Siemens, Uber, Vodafone and Whirlpool (see Figure 3)

Figure 3. Celonis Customers Include Global Leaders In Every Industry

Source: Celonis

Bottom Line: The Race To Business Graphs Is Here, Automation and AI are The Facilitators To Decision Velocity.

 The convergence of workflow, process mining, robotic process automation, integration services, microservices, and low-code/no-code platforms drive the future of software.  This next battle in enterprise software will be the creation of business graphs.  Like social graphs which use social networks to provide signal intelligence and digital feedback loops to accumulate massive amounts of data that is mined by AI, business graphs will accomplish the same for enterprises. 

In the case of the enterprise, the relationships among users, documents, business processes, and contextual data will power the signal intelligence and digital feedback loops.  As the majority of data is collected by digital feedback loops via automated and ambient collection, these systems can improve their precision decision capabilities.  Automation and AI are the tools that bring scale to creating decision velocity.

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The Gig Economy Comes to Customer Service

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Headlines these days are rife with news about the gig economy, whether debates on regulation, discussions about economic impact, or stock predictions. Occasionally there’s even an insightful analysis about the many different kinds of gigs and of gig workers. But the gig economy has so far had very little to do with the business of delivering customer experience. Which is why I’ve been so intrigued by Simplr.

A subsidiary of privately held insurance company Asurion, Simplr began operations in 2017 and seems to have hit its stride in 2020. From its origins tackling Asurion’s own customer service needs, the team behind Simplr saw an opportunity. Why not match variable volumes in customer service and information requests with a pool of well-educated experts seeking flexible work they can do from home?

Simplr’s agents—known as experts—don’t answer phone calls but do handle a range of written interactions. They aren’t dedicated to specific clients or product lines. That means they may be responding to tickets from multiple companies in a single session. It also ensures their time is used as efficiently as possible based on current levels of demand. Since most customer service teams are aligned to specific channels anyway, this also makes it easy for Simplr’s clients to incorporate it into their overall staffing management.

Technology Enables Better Human Interactions

From the outset, the team at Simplr sought to provide great customer service interactions through a combination of skilled staff and sophisticated use of technology. A straightforward user interface for experts presents a consistent work environment despite answering queries for multiple companies. API integrations into a wide array of customer service and CRM systems make it easy for Simplr’s client organizations to integrate into their existing systems.

Experts only have access to limited amounts of personally identifiable information and customer data, significantly reducing security concerns. Any requirement to access such information requires a specific request from the expert, triggering a recording of the interaction.

Simplr has also invested in incorporating AI across a number of key areas, using it to:

  • Detect and tag intent
  • Rate the complexity of each ticket
  • Match tickets to agent preferences and expertise
  • Present the relevant information and guidance to the agent

For Businesses, the Advantages Are Clear

Simplr provides companies with a pay-per-resolution model to address text-based customer queries, including email, chat, and social direct messages. If Simplr’s experts don’t resolve the issue, Simplr doesn’t get paid. The company aims to address the 70-80% of customer tickets that are predictable and routine, leaving in-house and full-time customer service teams to handle more complex, involved, or unanticipated queries.

The core value proposition delivers variable costs in line with variable demand. For businesses struggling to keep pace with the wild unpredictability brought by 2020’s global pandemic, that is a strong proposition indeed. Even better when it’s matched with a concerted effort to ensure that experts speak in the tone and language unique to that business. This ability was particularly important for one client, a well-known footwear company. The business’s quirkiness and personal touch underpin its whole brand.

Tapping into a variable pool of resources that sound just like its full-time agents has been a key element of success. At the same time, lightening the load for the in-house customer service and development teams has helped manage the transition as huge volumes of business shifted online. The company’s reliance on Simplr experts has scaled up and down along with the volume of demand.

Who’s Doing This—And What’s in It for Them?

A large proportion of Simplr’s workforce are military dependents all over the U.S. and abroad. The typical expert is a well-educated U.S. citizen (a good many have advanced degrees) who demonstrates strong problem-solving abilities. These are people who value flexible work schedules and can resolve or escalate customer queries quickly. That’s particularly important since experts are, like Simplr, compensated on the number of tickets resolved.

From my perusal of Glassdoor, satisfaction levels seem to be high among Simplr experts. Unsurprisingly, the ability to define work schedules and duration for themselves ranks high among the job benefits. The biggest complaint—and it’s a valid one—is not getting paid for supporting a customer well if the ticket eventually gets escalated anyway.

In theory, this problem should be minimized over time. As the AI tools get better at identifying which customer queries are likely to need escalation and which aren’t, the system should get better at filtering out the likely escalations. Similarly, agents themselves learn as they go, undoubtedly improving their own ability to decide to escalate a customer query early, before they’ve invested much time in it. Even so, Simplr may need to revise its payment policies to ensure that incentives for experts remain closely aligned to its clients’ priorities.

It’s A Win-Win We’ll See More Often

I’ll admit to being initially skeptical of Simplr’s capabilities. There are so many ways that something like this could go wrong—think poorly deployed bots or customer service agents who are nice but can’t actually help you. But I’ve been won over by Simplr’s specific and intentional focus as well as feedback from its customers. As one put it, this isn’t the cheapest option and it isn’t the most expensive—but the ability to scale up and down as needed, with the confidence that the customer experience is consistent with what’s delivered in-house, has been crucial. The benefits for both businesses and agent experts are well aligned.

So far Simplr is the only one of its kind in the market. The company is blazing a trail that others will likely follow, but Simplr has a big advantage in its ability to deliver effectively. With home working and the massive shift to digital commerce across all sectors here to stay, expect to see more of Simplr in 2021.

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News Analysis: ServiceNow Gets Serious About AI With Element AI Acquisition

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Element AI Takes Workflow to the Autonomous Level for ServiceNow

On November 30th, ServiceNow announced an estimated $500 (CAD) million acquisition of Montreal, Canada based startup, Element AI.  Co-founded by Dr. Yoshua Bengio, a 2018 winner of the Turing Award, Nicolas Chapados. and Jean-François Gagné in 2016, the startup’s mission was to bring AI to non-tech companies in order to compete with tech based companies (see Figure 1).  Key products include an AI-assisted insurance underwriting workflow software known as Underwriting Partner and a data set management platform for manufacturers known as Knowledge Scout.

Figure 1. Jean-François Gagné, co-founder and CEO of Element.AI

Source: Element.AI

In a blog post, Element AI”s co-founder and CEO noted "Element AI will help ServiceNow deliver workflows that learn more efficiently from smaller datasets, improve the quality of existing AI capabilities like content and language understanding, and expand new capabilities like image recognition and cognitive search.”  Gagné also pointed out this level of integration would help users "summarize information, make predictions and recommendations, and automate repetitive tasks,"

As ServiceNow’s fourth AI acquisition in 2020, CEO Bill McDermott and Chief AI Officer, Vijay K Narayanan, has doubled down on the investment and future of the platform with the build out of Now Intelligence capabilities along with the acquisitions of Loom Systems, Passage AI, and Sweagle.

Acquisition of Element AI Elevates ServiceNow Into The AI Driven Orchestration and Automation Market

AI changes enterprise automation holistically with its ability to automate manual labor through software. For ServiceNow, that is on the quest to become the universal workflow platform for the enterprise, it is the game changer that will attract CxOs to its platform. Any disruptor needs to show automation benefits, and delivering automation from AI can the difference maker for enterprises to adopt and migrate to new workflow platforms. And even for existing customers, in the ServiceNow areas of IT, customer and employee workflows, AI is a key change agent for a better automation future. And enterprises that automate more efficiently and become more agile, practice Enterprise Acceleration, the key fuel to enterprise survival in disruptive and tumultuous times. 

  1. Element AI addresses several product gaps and creates key opportunities – On the functional side ElementAI brings strong productized document processing to ServiceNow. It has good document processing capabilities that are part of its Document Intelligence product and on top of that its Knowledge Scout product creates knowledge models and workflows. Access Governor applies the knowledge from documents and recommends role based access for the enterprise. All three offerings provide immediate value for ServiceNow's customers.

    Element AI has done a good job also monetizing its APIs to enterprises, an offering that is less likely to remain around now with ServiceNow, as we don't expect the vendor will show interest in regards of AI related advisory services (beyond the immediate product scope of ServiceNow). Of substantial value will also be ElementAI's OS platform – if ServiceNow manages to empower a broader range of users to build AI based next gen Apps. If Orkestrator is of value remains to be seen – given ServiceNows partner strategy, it is more likely the vendor will most likely use its cloud partner's platform and orchestration platforms. 
     
  2. Element AI is an acqui-hire – Talent plays a key role in the battle for AI, and ElmentAI brings key talent to ServiceNow – effectively soaking up most of Canada's AI royalty from both a research and commercial perspective. Given Canada's substantial role in deep learning is a good move as well, but ServiceNow needs to find ways to keep the talent on board. ServiceNow may face some challenges retaining the team as AI talent is in high demand.
     
  3. Documents lead the way to workflow – ServiceNow has understood that documents are the artifacts of the digital enterprise – and are like the start and finish locations of a cycling stage race… with workflow the race between the start and end points. A lot of workflow can be derived from documents and that is where the Element AI IP comes in handy, with its document centric capabilities.
     

Customers Finally Gain A Future Roadmap Beyond Workflow

While many industry watchers would agree that the move to embrace workflow as the key messaging point has helped shift the perception that ServiceNow is both more than an ITSM offering and a key cloud platform. However, the larger differentiator in the next 18 months will come from both automation and AI.

  1. ElementAI goes from solution to ingredient – Element AI customers have found a viable future for the vendor that was in need to find another funding round. But the concern will be what will happen with offerings and services that are outside of the likely interest of ServiceNow. Customers relying on these offerings and services need to get reassurance from Element AI asap – better even from ServiceNow. Understanding strategy, product roadmap future and possible end of life plans and deadlines will be key.
     
  2. Servicenow gets more heft in AI – It's good to be a ServiceNow customer right now, as the vendor is expanding its automation capabilities and enterprises get more value from a proven / trusted platform. It also helps that IT in most cases runs it and there is no gatekeeper role here for LoB deployments – au contraire we see IT pushing new ServiceNow offerings. Element AI brings critical capabilities to ServiceNow, and now it will be all about how ServiceNow will shape the future of workflow. AI is certainly a key part of that and ServiceNow's ability to execute is now significantly increased. But acquisitions are one thing, delivering on roadmaps is another – and that's what ServiceNow customer need to keep an eye on. 

Platform Considerations Highlight The Challenges Ahead

ServiceNow follows a multi-cloud strategy with partnerships available or in the making wth the three big cloud providers. Element AI has catered more to the on premise work of data scientists, so there is no conflict here, as it makes its solutions portable, and likely to be easily integrated into ServiceNow's portfolio. 

Like all enterprise application vendors, ServiceNow needs to make a few key decisions on platforms when it comes to AI.  Constellation sees three crucial areas that need to be address:

  • Data gravity is real. AI needs massive amounts of data, but moving data is expensive.  Bringing the AI closer to the data is a key design point. With cloud data egress costs being punitive enterprise need to understand and plan where their data will reside. 
  • Customers need an abstraction layer to power AI. The heavy lifting is too complex for most enterprises and there are not enough data scientists out there. The democratization of AI is key for enterprise application success with AI and vendors need to provide a common layer for non technical users to own their automation destiny. 
  • Model migration strategy must be addressed. When models cannot be built where the data is, the need to be migrated and tested with the new data. Ideally this is avoided, but for most vendors still a reality.

Bottom Line: The Race To Business Graphs Is Here, Automation and AI are The Facilitators To Decision Velocity.

 The convergence of workflow, process mining, robotic process automation, integration services, microservices, and low-code/no-code platforms drive the future of software.  This next battle in enterprise software will be the creation of business graphs.  Like social graphs which use social networks to provide signal intelligence and digital feedback loops to accumulate massive amounts of data that is mined by AI, business graphs will accomplish the same for enterprises. 

In the case of the enterprise, the relationships among users, documents, business processes, and contextual data will power the signal intelligence and digital feedback loops.  As the majority of data is collected by digital feedback loops via automated and ambient collection, these systems can improve their precision decision capabilities.  Automation and AI are the tools that bring scale to creating decision velocity.

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

Monday's Musings: Inside The Post 2020 Election GeoTech Analysis

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What’s Next For 2021?

Join this special video to learn what happens in the post-election world with regards to geo-politics and technology.  The esteemed guests include:

Dr. David A Bray, Director, GeoTech Center and Director, GeoTech Commission at the Atlantic Council

Dion Hinchcliffe, VP and Principal Analyst of Constellation Research, Inc. | Tuck School of Business CIO Roundtable | ZDNet Contributor

R “Ray” Wang, CEO of Constellation Research, Inc. | Non-Resident Senior Fellow at the Atlantic Council

Key topics discussed addressed cover questions such as:

  1. So what does this all mean with a Democratic President, potentially Republican Senate, for the US, the World?  
  2. Where does the US vs China relationship go?
  3. What can we learn from Elon Musk?
  4. Does GDPR apply to space?
  5. What will happen with the future of digital cooperation?
  6. How has technology eroded human rights? What rights does a baby in 2040 have upon birth?
  7. Where will we be on our right to digital privacy?
  8. What should data policies look like?
  9. Will digital avatars play a role in the future?
  10. How has economics failed to explain what’s really going on in this digital world?
  11. What happens in a culture of abundance?  Will we be efficient?
  12. How will we make this work for the 8 billion people on Earth?

The Hope For Clear Leadership And Stability Emerges

Ray – Don’t expect massive change given our federal system. Policy and themes may set direction but there is only so much the federal government can do. States and localities will still have a lot of local control.  This balance of power ensures stable government.

Dion – Most organizations are extremely complex systems that make it hard for a single level of control to drive change.  The Influencer leader is emerging as the most effective agent.  Clear and stable leadership is key.  Servant leader is the model where we are emerging as the most effective.  The world is becoming a place where there is limitless opportunity and we seem to be evolving mostly on the technology side but not the rest of the other areas. 

David – Tech policy and relationships will change.  Europe and NATO wants to know how to work best with the Biden administration.  The last four years has seen a decoupling of Europe and American over the past four years.  A new coalition and community of digital democracies seek to bring closer ties.  This coalition builds on those open societies who like choice and promote freedom not oppression from technologies. 

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Facebook Picks Up Kustomer to Accelerate Chat-to-Commerce Services

With widely anticipated U.S. federal antitrust lawsuits looming, it may seem like an odd time for Facebook to make another big acquisition. In Kustomer, however, Facebook is buying itself options. Here is what we know so far:

  • According to reports, Facebook has agreed to buy New York-based Kustomer for $1 billion. In a blog post announcing the acquisition, Dan Levy, Facebook VP of Ads and Business Products, and Matt Idema, COO, WhatsApp stated that, “Our goal with Kustomer is simple: to give businesses access to best-in-class tools that deliver excellent service and support.”

  • Kustomer, founded in 2015, bills itself as a customer service centered CRM platform. With the mantra of “customers not tickets”, the platform delivers service and support capabilities in a single timeline, centralizing a customer’s experience and engagement with a brand into a consolidated view.

  • Kustomer has an AI powered “Kustomer IQ” tool that embeds across the platform, managing everything from intelligent routing of service engagements to aiding in the management of repetitive tasks and deflection of easy, frequent, and repetitive questions.

  • Some of Kustomer’s key logos include Ring, Glovo, Glossier, Sweetgreen, Away, Rent the Runway, and UNTUCKit.

  • Kustomer CEO and Co-Founder Brad Birnbaum is a customer service and community chat veteran and no stranger to successful acquisitions as he previous co-founded Assistly, acquired by Salesforce, and co-founded eShare Technologies while still in college.

This acquisition gets interesting for Facebook on several different levels.

Kustomer adds customer service capabilities that round out Facebook Shops and Facebook Marketplace. Facebook Shops was introduced in May 2020, initially to empower small business customers to quickly transition from a brick-and-mortar only business to an e-commerce storefront with a built-in marketing and advertising channel. Shops’ full suite of solutions help get everything from inventory to merchandising and marketing under control and were rolled out to all businesses in August. Facebook has seen a steady increase in traffic and utilization of both Shops and its primarily Peer-to-Peer commerce offering, Marketplace, in the last three quarters, leading to a robust Q3 revenue report that exceeded expectations.

With Kustomer, Facebook can directly meet the needs of its small and medium business customers who do not already have digital customer service capabilities. That’s likely the majority of Facebook Shops customers that just recently made the move to e-commerce.

Omnichannel messaging capacity powered by AI and built for volume. For Facebook, the big win in Click to Shop has been frictionless commerce experiences where customers don’t have to leave Facebook to shop. Customers can view products and transact directly in chat messages in Facebook Messenger, Instagram, and WhatsApp. Kustomer’s service capabilities extend the scope of customer interactions directly within Facebook even further. This turns Facebook’s end to end social commerce vision into a reality.

Kustomer migrates to storing customer’s data on Facebook’s infrastructure, rapidly advancing its cloud hosting services. In the fine print of the acquisition announcement, Facebook notes that it “eventually expects to host Kustomer data on secure Facebook infrastructure. In doing so, Facebook will act as a service provider at the instruction of business customers.”

Given Facebook’s announcement in October 2020 that it would enter the cloud computing sector, it has a strong vested interest in generating more customer service messages through Facebook channels. Storing those messages—at a cost that will likely range from half a cent to 9 cents per message, according to a Reuters interview with WhatsApp’s Idema—generates an additional new revenue stream.

The Elephant in the Room

Facebook has a lousy reputation when it comes to the security and sanctity of customer data. The company’s failings include a track record of failing to secure customer accounts and miscalculating metrics used to calculate advertiser payments. Even in the October cloud storage offering announcement, Idema was quick to note that the messages being stored via the new hosting service are “stored elsewhere and not protected by the app’s end-to-end encryption.”

Despite Facebook’s desire to immediately claim the status of “service provider” for customer data solutions, this comes with significant risk. The increasingly white-hot focus of regulators from California’s CCPA/CRPA to the EU’s GDPR leaves far fewer corners to hide in as Facebook enters the CRM jungle.

What This Means for Kustomer and Its Customers

The intention, for now, appears to be to allow Kustomer to remain own entity under the Facebook umbrella of brands. That’s sensible given how important ongoing innovation will be to ensuring Kustomer remains a credible contender in a crowded and competitive market, as well as the importance of the many integrations other CRM platforms have to Facebook channels.

Since Kustomer’s capabilities concentrate on customer service interactions, integration with Facebook’s existing commerce and advertising capabilities should help it compete with broader, more developed CRM offerings. Most, if not all, of Kustomer’s existing customers already use Facebook channels, so the impact of the acquisition should be neutral to positive. We’ll be looking out for any new pricing and bundling options that might be introduced.

Bottom Line

In acquiring Kustomer, Facebook has bought itself two important assets: 1) additional revenue streams through expanded services to small and medium businesses and 2) a wider range of strategic options depending on what antitrust lawsuits might come its way. 

As with all things Facebook, the devil and his evil ways are ALWAYS in the details. There are significant upsides in customer service, support, and engagement that customers can achieve with a Facebook-Kustomer combination. The test will be if Facebook, flaws and all, is ready to take customer data as seriously as it will need to in the world of data service providers, or if it will still assume that its tall garden walls are thick enough to take on the new legal, compliance, and ethical burdens.

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AWS Re:Invent 2020: Week One Database, ML and IoT Highlights

Amazon Web Services Re:Invent 2020 continues the vendor’s tradition of delivering a slew of announcements. Here’s my take on the top data-to-decisions introductions.

Where to begin to size up the more than 35 announcements slated for just the first week of Amazon Web Services (AWS) #ReInvent 2020? Guided by my data-to-decisions (D2D) research focus, let’s drill down on what I see as the highlights among the database, machine learning, and intelligent edge/IoT announcements from week one of this three-week virtual event.

Amazon Web Services CEO Andy Jassy kicking off Re:Invent 2020.

(I’ll skip over all the new compute and storage instance types as well as the container-management, serverless computing and hybrid deployment announcements. Not that these offerings won’t have big impacts on D2D deployments -- they most certainly will -- but the highlights will, no doubt, be covered in detail by my colleague, Holger Mueller.)

‘Glue’ing Together the Database Services

AWS’s database strategy is to deliver a fit-for-purpose service for every need, as exemplified by Amazon RDS (relational database services), DynamoDB key value store, Amazon ElastiCache in-memory store, Amazon Neptune graph database, Amazon DocumentDB, and the Amazon TimeStream time-series database. AWS says customers want “the right tool for the right job to optimize their workloads.”

In contrast, AWS rival Oracle emphasizes that graph, JSON (document), and time-series capabilities are all built into its flagship Oracle Database. But the success of role-specific databases (from AWS and others) strongly suggests that there are many cases where the performance and depth and breadth of functionality from task-specific products and services matters. The downside of using separate services is that you can end up with silos of data. This problem triggered what I see as the most significant database-related announcement from Re:Invent:

  • AWS Glue Elastic Views. This new service, in preview as of December 1, is designed to automatically combine and/or replicate data across multiple data stores, creating a virtual table (or materialized view) in a target data store. You could, for example, combine data from multiple stores and copy it into Redshift for analytical use or ElastiSearch for searching. AWS Glue Elastic Views monitors source stores and updates the target automatically “within seconds,” according to AWS. The preview works with DynamoDB, S3, Redshift and ElastiSearch, and support for Aurora is said to be on the roadmap. MyPOV:  The longer the “works with” list becomes, the more powerful and attractive this feature will be. Customers will undoubtedly clamor for Aurora support and other RDS options.

Here are two other notable database-related Re:Invent week one announcements:

  • Amazon Aurora Serverless v2. This advance over Serverless v1 is touted as brining greater elastic scalability as well finer-grained (cost-saving) scaling increments to Aurora. MyPOV:  Elastic, serverless scaling is big cost saver as compared to conventional provisioning for peak workloads. Customers asked for, and are now getting, capabilities for more demanding Aurora deployments through Serverless v2.
  • Babelfish for Amazon Aurora PostgreSQL. Don’t let the name confuse you: Aurora already offered PostgresSQL “compatibility.” Babelfish is a new translation layer, in preview as of December 1, that will enable Aurora PostgreSQL to understand Microsoft SQL Server T-SQL. Babelfish enables applications built using T-SQL to “run with little to no code changes,” according to AWS. The goal is obviously to accelerate migrations from Microsoft SQL Server to Amazon Aurora. Once moved, you would write to PostgreSQL. MyPOV: Keep in mind that AWS database “compatibility” (be it Aurora with PostgreSQL or MySQL, Keyspaces with Apache Cassandra, DocumentDB with MongoDB or, now, Aurora PostgreSQL with Microsoft SQL Server) is a one-way proposition. Once you migrate, you are running on an AWS database service and there’s no easy migration path back to the database you formerly used. Be sure you will be fine with the reality that there’s no easy way to go back.

Filling in the SageMaker Gaps

AWS made huge strides forward on the ML front over the last year with the introduction of SageMaker Studio (based on JupyterLab), SafeMaker Autopilot, SageMaker Debugger, SageMaker Model Monitor and more – all announced at Re:Invent 2019. At Re:Invent 2020 AWS plugged some of the few remaining holes:

  • SageMaker Data Wrangler is, as the name suggests, a self-service data-prep option that will enable a broader base of users (not just data engineers) to handle the data cleansing and transformation tasks that are necessary before you attempt to build reliable models. The tool set promises to make data-prep workflows repeatable and automated. MyPOV: Obvious and overdue, but, nonetheless, welcome. I’m eager to hear more about the depth and breadth of capabilities.
  • SageMaker Feature Store is aimed at getting more mileage out of the work that goes into feature engineering.  As with any good repository, this service is designed to help you store, discover and share – in this case features for use across multiple ML models. It’s also said to help with compliance requirements, as you can recreate features at a point in time. MyPOV: Data science skill is scarce, so any advantage available in reusability and productivity is welcome.  
  • Amazon SageMaker Pipelines is designed to build automated ML workflows at scale, cutting development-to-deployment time from “months to hours,” according to AWS. These automated pipelines are said to be easily auditable because model versions, dependencies and related code and artifacts are tracked. MyPOV: Automation is the key to bringing ML into production at scale, so this is a welcome announcement.

Improving Industrial Processes With Edge Intelligence

Machine learning and machine vision are the core of four new industrial IoT services introduced by AWS:

  • Amazon Lookout for Equipment is an ML-based anomaly detection service for industrial equipment. In preview as of December 1, this service is focused on predictive maintenance for assets such as motors, pumps, turbines and more.
  • Amazon Monitron is a complete, end-to-end monitoring system for industrial assets that includes sensors, a gateway device and ML services to detect abnormal conditions. Now generally available, Monitron also includes a prebuilt mobile app that tracks the status and health of assets without the need for mobile app development or model training.
  • AWS Panorama is a small-footprint appliance that’s installed locally to run custom computer-vision models at the edge for real-time decisions in applications such as quality control, part identification and workplace safety. Now in preview, AWS Panorama includes a software development kit designed to support pre-existing or Panorama-compatible camaras, integrate with AWS ML services, and support model development and training in SageMaker.
  • Amazon Lookout for Vision is used to develop models to spot defects and anomalies using computer vision and can be used at the edge in conjunction with the Panorama appliance. Computer vision models used to detect damage, evaluate color, identify missing components and other machine vision inspection needs can be trained with as few as 30 images, according to AWS.  

MyPOV on Industrial Advances: Predictive maintenance and quality control are the most in-demand, starting-point applications in IoT and machine vision deployments, respectively. AWS has formidable competition on these fronts from Microsoft Azure, so getting more industrial customers in at the top of the funnel by lowering time-to-value for high-demand applications is an important objective. The IoT arms race is far from over, and AWS is also this week introducing improvements to its Marketplace that will make it easier for customers to work with third-party IoT partners and service providers.

MyPOV on Re:Invent 2020

The rule of thumb for many virtual tech event organizers in 2020 has been to cut down on the presentation time and the volume of keynote content that would have otherwise been delivered in an in-person event. The idea is that virtual attention spans are virtually nonexistent, but AWS apparently didn’t hear or heed the advice on timing. AWS CEO Andy Jassy’s opening Re:Invent keynote once again clocked in at nearly three hours.

As for the content, AWS once again delivered a juggernaut of new product introductions and announcements in just the first week, but qualitatively, the announcements -- at least those in the database and ML arenas - don't feel as substantial as those in 2019. As noted above, AWS delivered quite a bit last year, so the approach of filling in gaps and introducing v2 improvments is likely to be welcome to cutomers, many of whom are still adjusting to working with remote teams and trying to make the most of the AWS services already in use. In fact, in years past, I've encouraged AWS to focus more on quality, fit and finish and less on the quantity of Re:Invent announcements. Also on the topic of making the most of what's already available, I really like some AWS Marketplace announcements, coming later this week, that will make it easier to work with third-party software and service providers in Amazon's cloud.

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Product Review: Apple's M1 MacBook Pro 13"

Media Name: rwang0-apple-macbookpro-m1-13.png

When They Meant One More Thing, They Meant One More Big Thing

Apple made ground breaking announcements at its “One More Thing” event on November 10th, 2020 event.  The launch of the M1 chip, macOS Big Sur operating system, and a long awaited update to the Mac line up.  Apple takes its destiny into its own hands from silicon to services.  By having complete control of the chip, operating system software, and hardware, Apple delivers exponential gains in performance, better value, longer battery life, and significant improvements to the Mac line up.

Mac mini, 13-inch MacBook Air, and the 13-inch MacBook Pro sport the new Apple-designed M1 chip (see Figure 1).  This 5nm system on a chip (SOC) delivers Apple’s own design and architecture which pulls the processor, GPU, DRAM, fabric, cache, and Neural Engine, and security features, all on one chip.  With the power of 16 billion transistors, the chip blows away the competition when looking at performance per watt.

Figure 1. Inside Apple’s M1 Silicon On Chip (SOC) Specifications

Source: Apple

Compared To The Intel MacBook Pro 13″, The M1 MacBook Pro 13″ Has Many Advantages

When the M1 MacBook Pro 13″ is compared to the Intel MacBook Pro 13″, the obvious similarities come from the familiar chassis design and key display features (see Figure 2).  Both models share:

  • Availability in Space Gray and Silver
  • 13.3-inch LED-backlit display with In-plane switching (IPS) technology, wide color (P3) and True Tone
  • 720p FaceTime HD camera
  • Wide stereo sound and support for Dolby Atmos playback
  • 3.5 mm headphone jack
  • Magic Keyboard
  • Force Touch Trackpad
  • Touch Bar and Touch ID
  • Bluetooth 5.0

Figure 2. Everything You Need To Know About The M1 MacBook Pro 13″

Source: Apple

Here are the differences between the two models (see Figure 3):

Figure 3. Side by Side Comparison of Intel-based vs M1 Apple SOC-based MacBook Pro 13″ 

Source: Constellation Research, Inc.

Overall, the M1 MacBook Pro beats out the existing Intel line up.  However, a few areas of improvement for future releases include the need for a better than 720p FaceTime camera (though the M1’s ISP does improve the performance of the 1.2 megapixel camera), Wi-Fi 6 support, and the lack of a touch screen.

Compatibility With x86-64 Apps Remains A Work In Progress

Initial tests on the M1 MacBook Pro have yielded great performance and exponentially improved battery life when running native Apple apps.  However, slight glitches and significant performance hits occurred with application crashes wtih some of the enterprise products from Microsoft including Microsoft Outlook, Microsoft Teams, and Microsoft Edge browsers.  The Microsoft Office suite didn’t show any lag at all when used and Microsoft Excel macros seemed to experience improved performance.

Other enterprise software such as Autodesk Revit, Avid ProTools, Google Drive File Stream, MatLab, Oracle VirtualBox, Parallels, and VMWare Fusion currently are not supported by the M1 chip.  Initial experiences with much of the Adobe Creative Cloud suite show some compatibility issues with Rosetta 2 for Adobe AfterEffects, Illustrator, Indesign, Photoshop 2020, and Premier Pro.  Ongoing fixes and updates can be found on the Adobe web site.

Slack really slacked and needed a performance hit.  Video software such as BlueJeans, GoToMeeting, WebEx, and Zoom all were quite laggy.  Meanwhile, DJ controller software such as RekordBox by Pioneer wasn’t working yet on Rosetta 2 or M1 as of this post. Enterprise clients should confirm which apps work with the new M1 architecture and ask for a roadmap from key vendors for M1 forward and backward compatibility on Rosetta 2. 

Early adopters must determine the trade-off of native Apple performance versus x86-64 Intel compatibility.  The independent crowdsourced site “Is Apple Silicon Ready” also confirmed some of the incompatibilities and performance issues with M1 and Rosetta 2 experienced in this review.  (Note: this site is not an Apple-run or reviewed site, and the accuracy and timeliness of information it offers is not officially verified)

The Bottom Line: The New M1 Macs Are A Future Proof Investment

The fully integrated control of the M1 chip, macOS BigSur, and Mac hardware kicks off the beginning of a new era for Apple.  The venerable hardware innovator now has full control of its ability to differentiate on product, performance, price, and place. As Apple Silicon makes its way into the full line up of hardware, users can expect significant advantages over commodity and purpose built PCs.  Moreover, the announcements made at the 2020 Worldwide Developers Conference and the November 10th event bring the convergence of iOS and macOS one step closer. 

However, much work needs to be done on backward compatibility as early users and tests show a lot of broken apps to be addressed. Overall, the M1 MacBook Pro 13″, Mac mini, and MacBook Air are a wise investment into the future. In conversations with the major software vendors, Constellation expects backward compatibility to improve as major software vendors  adjust and improve their offerings to support the new line up over the next 18 to 24 months.

Early adopters and Apple shops looking to modernize their computing should consider the M1 MacBook Pro 13″, Mac mini, and MacBook Air in their procurement refresh cycle plans and short lists over the next 24 months. Why? This is the next era of computing for Apple and the long-term benefits will significantly outweigh the costs.

Source: Apple

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Adobe Acquires Workfront, Focusing Squarely on the Work of Marketing and Customer Engagement

On Monday, November 9, Adobe announced that the company had reached a definitive agreement to acquire Workfront, the enterprise work management platform, for $1.5 billion.

According to the press release, Workfront CEO Alex Shootman will lead the Workfront team and report into Anil Chakravarthy, executive vice president and general manager, Digital Experience Business and Worldwide Field Operations, once the deal closes. That’s expected to happen within the first quarter of Adobe’s 2021 fiscal year, which starts December 1, 2020.

Adobe and Workfront had a long-standing partnership prior to the acquisition. That relationship includes APIs that connect Workfront to Adobe Creative Cloud and Adobe Experience Cloud. Of Workfront’s more than 3,000 customers, over 1,000 are also Adobe customers. Workfront currently has one million users across those 3,000 customers.

Adobe + Workfront—A Better Way to Workflow?

Functional silos that stymie progress consistently top the list of roadblocks to marketing and engagement success. Adobe’s proposed acquisition of Workfront squarely takes aim at the internal silo walls that disrupt marketing operations. The move also adds some much-needed operational collaboration to Adobe’s suite of marketing tools, Experience Cloud.

But what happens beyond marketing? Adobe had made huge strides across Creative Cloud in facilitating collaboration and workflows across creative development, from design briefs to final outputs and beyond. The recent Adobe MAX virtual conference focused heavily on these new developments and how they help both hard-core creatives and the people on the front lines responsible for getting the right assets to the right places.

Will this functionality cement marketing as its own worst enemy by driving selective collaboration exclusively within marketing or will it bridge critical collaboration gaps across departments despite different work styles and structures? In an age when customer experience (CX) is not the exclusive domain of marketing, could this create unintentional isolation from other critical front-line collaborators or encourage a culture of CX as a team sport? Or, will it serve to blur the hard lines between roles, management and methodology and firmly plant the customer as the unwavering North Star for CX strategy and campaign execution? We'll be watching closely to see how ambitious Adobe’s vision for work proves to be outside of its marketing, commerce and creative footprint.  

The Allure of Workfront

Don’t call it “just” a workflow manager. While Workfront initially made a name as a project management and collaboration tool, it has increasingly become known as a work management platform. It orchestrates strategic planning, team alignment, creative collaboration and resource planning, focusing on the people doing the work and the ways in which they need and want to connect.

Users believe Workfront truly shines in aligning the complex workflows, processes and foundational work that goes into massive creative projects. That enables teams to work together to get assets out the door faster, more closely align to strategy, and land their efforts with greater impact. With an extensive library of out-of-the-box integrations, Workfront quickly positioned itself as the “effectiveness engine” for the modern marketing operational machine so critical to keeping digital business moving forward.

Workflow’s use case extensions have broadened over time, including the overarching management of digital transformation by helping technology teams to focus on the right projects to take on and the right decisions to make. As a result, IT teams, agency partners, and contractors work more effectively together to deliver the right projects, on time, and with greater impact for the business. In the end, although Workfront has remained dedicated to accelerating and demystifying the work of marketing, its true potential lies in the collaboration required to shape consistent customer experiences—and that reaches far beyond marketing.

Why This Matters for Adobe and Its Customers

Adobe has existed as two spheres of the marketing brain: the wild, unbridled creativity harnessed in Creative Cloud and the rapidly accelerating engine of customer engagement and connection in Experience Cloud. While the two sides of this cloud often connected and clearly influenced one another, workflows, governance of assets, and strategic planning have often failed to flow across the entirety of the creative-to-experience continuum. The addition of Workfront as the epicenter for work helps bridge the gap in planning, understanding and optimizing the entire lifecycle of digital experience from inception to optimization.

For Adobe, this fits the pattern of innovating on behalf of the marketing organization and being the champion for digital business. The work of marketing has distinct requirements and characteristics that set it apart from other types of work. As organizations become more attune to the reality that different types of work have different requirements, tools like Workfront that are intentionally developed to suit a distinct type of work or way of working become that much more important to success.

As recently introduced at Adobe Max, Creative Cloud has investigated, interrogated, and celebrated workflows that more freely bring creatives into a more data-rich strategic dialogue with marketers. Adobe has an opportunity with Workfront to further extend this connection between creatives and marketing execution. If this acquisition lives up to its potential, Adobe will strengthen workflows across Customer Experience Cloud and Adobe Experience Manager. We see this acquisition as a clear indication of Adobe’s desire to empower the engagement lifecycle by documenting progress and impact, aligning around strategy and success, and eliminating the vagaries of gut and instinct.

There’s Clear Upside for Workfront Customers

Workfront has faced some criticism for deficiencies in interface usability, its digital asset management (DAM) capabilities, and pricing. Current Workfront users will see a BIG improvement with access and increased connection to Adobe Experience Manager Assets, Adobe’s DAM solution. It is rich in functionality and, perhaps most interestingly for this acquisition, able to deliver tangible data into asset development, utilization, and engagement. In short, Workfront as part of Adobe gets better.

Bottom Line

We get excited about seeing a future where Sensei is unleashed for insights into rapid decision making, automated workflow updates, and perhaps, measurements around the impact and connection between effective work and effective engagement. Companies need to accelerate the pace at which this work gets done. The right tools can tackle the stagnation of decision making and the lag between ideation and iteration. And that could help unravel the cultural juggernauts that intensify the friction between creation, deployment, and measurement.

There’s tremendous potential in this acquisition, but we still have questions and concerns. Chief among them:

  • How does this fit into Adobe’s broader partnership strategy, especially relationships with Microsoft and ServiceNow?
  • What happens if Workfront stays siloed within Experience Cloud? Adobe’s track record on seamless integration of acquisitions has been hit or miss.
  • Most importantly, what does Adobe want to be when it grows up? Will the company use this opportunity to take the lead in redefining how the work of marketing and customer engagement are done across the board? Or will it focus on cozying up to its biggest partners and increasing its appeal as an acquisition candidate?
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The Return of the CIO Prerogative: A Best-of-Breed IT Landscape

When it comes to technology industry trends, the pendulums swing back and forth with steady frequency. Numerous examples abound but two will suffice here: a) the shift from on-premesis to cloud and then a good bit back to edge again, and b) the move from green screens to native PC apps then to the browser. Now when it comes to the way IT is acquired and situated in the enterprise today IT, the pendulum is currently swinging quickly back to a highly-valued old position that will very likely separate leading IT organizations from the also-rans going forward.

Years ago, when IT was much less pervasive, corporate uptake of technology was more varied, and the giant enterprise suites hadn't yet become the all-consuming platforms they've become, it was a simpler time. Back then the CIO put all the strategically differentiating IT into one bucket, and all the commodity IT that didn't move the needle much for the business into another. Then IT took the applications and systems in the differentiating bucket -- the ones that could break the business if done poorly, or would make/keep the business an industry-leader if done right -- were carefully selected, acquired, and customized to fit the special way the business carried out that function, integrated with key systems and then brought online.

Related: The CIO Must Lead Business Strategy Now

Great IT Helps Organizations Compete Better in the Market More Than Ever

As history is such a good teacher, particularly in the IT world, it's easy to look back to see that there were several problems that emerged from this approach.

First, integration was often the most time-consuming and expensive of all of these processes. The best IT systems don't create new data silos when that information already exists in other systems. But for most of the history of IT, integration was hard. It often delayed or even put the whole project at risk entirely through intractable integration issues discovered too late in the game. It wasn't until quite recently that lightweight integration approaches including open APIs, microservices, and now the giant new vendor graph APIs such as Microsoft Graph and SAP Graph emerged, which when combined with integration platform-as-a-service (iPaaS) like Dell Boomi, Jitterbit, Zapier, and Integromat (to name just a few) that made integration an order of magnitude less costly and time-consuming.

The Complexity Management Curve: App, SaaS, Cloud Growth is Taxing CIOs and IT to the Limit

The second problem was IT customization. In the days before cloud, vendors allowed on-premises IT systems to be customized because that's what CIOs wanted, for the differentiating reasons cited above. Though customization was often overdone, the argument itself was sound: Differentiating can really matter, especially around the systems that drive the core value proposition of the business. Yet the IT customizations of yesteryear were frequently stranded as the vendor upgraded the product, which often made the old customizations outdated or incompatible. This required expensive redevelopment and testing for each upgrade cycle. As we now know, it's much better to quickly iterate IT systems to keep up with the market and changing business needs. Yet customizations had the opposite effect, leaving most organizations trapped on rapidly aging versions of products. So customization as often abandoned. As much as it brought strategic value, it also required organizations to take on too much risk and cost, at least with the state of technology back then.

Second Wave DT Requires Difficult-to-Achieve High Velocity Change

Then cloud arrived, along with software-as-a-service (SaaS.) Now IT had competition, and real competition it was too. Apps could be quickly procured and deployed in weeks at lower price points, integrations were easier with standard, simple APIs via REST, and most vendors now have a growing directory of off-the-shelf integrations to make it easier and less costly. Even the entire data center could be made someone else's capital investment issue and maintenance headache.

But as organizations digitized everything more and more, it became clear that the most disruptive market differentiation was regularly demonstrated by cloud-native tech companies that were moving faster, innovating more, and creating more compelling digital offerings in the market. They were also doing this on more modern platforms, they maintained less techical debt, and provided new types of experiences that better appealed to businesses and consumers alike. We are now all familiar with what Netflix did to video rental companies, Amazon did to much of retail, what the iPhone has done to a dozen industries at once (media players, cameras, GPS devices, etc.)

Most businesses have discovered that they've digitized, but they haven't transformed, which take truly sustained and widespread effort on the technology front.

Now we're in a second age of digital transformation (DT), where every organization now has to be a fast-moving cloud-native tech company in order to compete. Unfortunately, most organizations have at least three primary issues that are holding them back on the technology side: a) Two types of technical debt, one in acquired infrastructure/apps and another in in-house enterprise architectures that are too old and out-dated to compete yet are also extremely costly to replace, b) no appealing migration path from this situation to a new and better future that doesn't have a ten year program plan behind it (something well outside the 2-3 year planning and relevancy window of just about any IT department), and c) a runaway IT landscape that today has organizations acquiring and using more apps at an apparently exponential rate.

If you could ask a CIO what their biggest challenge is today -- after the out-of-control, wildly unpredictable, and potentially career-ending threats of cybersecurity and ransomware -- it is this: IT has simply become too complex in its current form to guide and navigate as an overall portfolio. This is not a theory. It is borne out by recent data: As I explored recently on ZDNet on this subject, most CIOs now cite tech complexity as their #2 challenge overall.

Further complicating matters is that the major enterprise IT vendors have built out -- through both development and acquisition -- now massive enterprise suites, especially in ERP and in front office productivity -- which have individual offerings that tend to vary substantially in quality, maturity, and overall integration with the rest of the suite. If given an easy choice, CIO would like to mix and match best-of-breed parts from multiple vendors, but prior experience has shown it was too costly and risky.

CIOs also don't like to become beholden to too few vendors (who then hold the cards), for reasons of choice and having too many eggs in one basket. However, while they do like that the modules of enterprise suites do to work together a bit better and they only have one throat to choke for problems, it's increasingly becoming moot as more and more parts of the business punt locally and use a better tool for the job anyway.

What then can IT leaders do today to address all these factors?

Differentiation Comes Back with a Vengeance

Most CIOs would very much like to put the complexity genie back in the bottle. In fact, they'd like to make the IT landscape less complex for a whole host of reasons, a partial list of which incudes fewer data silos, more consistent user experiences, easier worker onboarding, lower support costs, less cognitive overload, more strategic differentiation, more business productivity, less vendor lock-in and an easier overall IT portfolio to manage and govern. Yet it's likely that this complexity will eventually grow beyond human ability.

Consequently, successful managing complexity, as I began to say a few years ago, is fast becoming an IT imperative, not a nice-to-have. Soon we will simply have too much homogenized IT that doesn't separate us meaningfully from the competition, but still accompanied by sheer data and experience sprawl that will make it very difficult to have workers learn and do their jobs effectively (other than perform whack-a-mole across many dozens of highly variable and poorly (or not-at-all) integrated apps.)

As I've argued recently, the starting point for addressing these challenge effectively requires a new and highly-related focus and mindset:  We must become experience driven organizations. Where to start in this view? We must focus on the most strategic and differentiating areas of customer, employee, and even partner experience.

However, putting the IT complexity genie back in the bottle is going to require a sea change in a few key parts of the IT landscape. One that would ordinarily seem unlikely to happen on its own. Yet happen it has with the arrival of highly capable entries in two relatively new product categories and one still emerging one. These have been directly enabled by the widespread adoption of APIs by SaaS and cloud platforms vendors. Combined, this has created an emergent industry breakthrough that has returned mix-and-match power to the CIO, as well as a way to combine and streamline app experiences into simpler, easier to train on and support solutions. Business data can easily be accessed wherever it's needed, while individual features can be extracted and moved to the places where they best belong, with the swift assembly of pre-existing data and functionality into compelling new point experiences. This approach can also be scaled better, because it uses high leverage new conceptual models that are as as manageable as they highly accessible, so that mixing-and-matching can be a daily activity carried out by a much wider group of citizen developers, instead of just IT.

Indeed, this self-service model, one of the key new ways to tap into much larger resources with low cost (such as end user peer support), has now arrived for integration and application development as well.

Three Key Enablers of Best-of-Breed IT: The Product Categories

While there are other supporting technologies in best-of-breed IT, the principle categories that are most influential in moving IT into a new mix-and-match posture, yet and also delivers on the experience imperative for more streamlined,  more-focused, and situated IT at high speed and scale are:

  • Integration Platform-as-a-Service (iPaaS). The industrialization of integration has arrived with platforms that have combine off-the-shelf integration catalogs with point-and-click "wiring together" and testing. Performance management, operations capabilities, and governance are also common in iPaaS. The best iPaaS even makes it possible for power end-users to integrate much of their data sources, with the aforementioned Zapier and Integromat being prime examples. With iPaaS, most organizations can quickly API-enable and integrate most legacy IT.
  • Low-code/no-code solutions. While spreadsheets have long been the low code tool of choice for the typical non-IT worker, they are entirely inadequate as a low-code model. The good news is that this has begun to change in a big way with the advent of solutions that bring application development-style sensibilities and features (testing and version control, for example.) Our regularly updated Low-Code ShortList which I maintain with my colleague Holger Mueller is a good example of how a cottage industry has grown in a major tech industry in its own right. Many analyses have shown that there will never be enough professional coders to digitize everything that needs to be digitized in businesses today. Nor does there need to be. Making it easier for workers to turn their tech dreams into reality is now the next step in IT. That is, as long as the necessary guardrails around data security, privacy, and regulatory concerns are incorporated into the model. Many of these platforms now directly address this need.
  • AI-based app generation. On the cutting edge, and without any clear category name yet, is what will likely prove to be the next major breakthrough in scalable IT differentiation. This is the creation of applications by using a capabile AI-based solution that can take high-level requirements in natural language, determine what the solution needs to do, then design, develop, integrate, and test it, and finally provide the completed app to the user for acceptance. While there are virtually no products in the commercial space currently available for this (yet), the impressible results of OpenAI's GPT-3 has shown that this breakthrough is not only possible but likely inevitable. The power this will confer on the average IT user is difficult to understate. It's is akin to literally verbally articulating the need for an app to solve a particular problem, and then having an AI agent immediately develop it.

Important Note: iPaaS and low-code/no-code are often used together to form what I refer to as a multicloud experience integration stack. This is an end-to-end strategic platform solution that helps deliver on best-of-breed IT assembly line with an experience-centric focus. Please see my exploration of this important new category of IT solution and the key players.

A Faster, Nimbler, Better-Fitting IT: Acquiring, Making, and Orchestrating Digital Experiences at Scale

A New Democratizing Craft Model for IT, But Without the Old Baggage

How then can the CIO begin moving back to best-of-breed IT? First, they will need to create a better API-level foundation, or best-of-breed is relatively inaccessible to them if on-premesis IT resources need to be glued together or mixed-and-matched (which is highly likely in any larger organization.) That's because while their cloud and SaaS vendors are largely providing the APIs for IT already, most legacy systems still lack them. Fortunately, solutions like Mulesoft and TIBCO Cloud Mashery can great help added the needed legacy APIs in a full lifecycle framework. Second is the mindset that IT must be made far more malleable and easily-reshaped in the middle-tier and in experience-delivery, and that those that can and should engage in this activity aren't always in IT.

Most IT organizations do very much want to go faster and do better with digital, and now they really can. Employee experiences can be created for each step in the employee lifecycle out of the whole cloth of existing IT. New customer experiences can be rapidly created that are highly contextual and personalized, using everything the organization knows about them to improve their digital journey. But IT departments will have to unlearn a lot of their own predispositions and pick up some new talents along the way (especially low code education and application lifecycle management on the edge.)

That all this can be done by most organizations, there is little question. In fact it has been happening in the margins of many IT departmenst for years already. Now it must move from the margins to the center of IT. Ultimately, there is a clear and substantial business case in being more intentional and programmatic about creating a faster, nimbler, more malleable best-of-breed IT department. It's now up to the CIO to drive this agenda.

My research on this topic:

The art of the possible: Pervasive integration of enterprise systems and data arrives | ZDNet

To Strategically Scale Digital, Enterprises Must Have a Multicloud Experience Integration Stack

Digital Transformation Target Platforms ShortList

Why Microservices Will Become a Core Business Strategy for Most Organizations

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News Analysis: Oracle Allegedly Wins Bid For TikTok

Media Name: rwang0-oracle-tiktokus-larryellison-zhang-yiming.png

Why This Makes Sense On Three Counts

If the Wall Street Journal's article is correct, Oracle has emerged as the winner over Microsoft and Walmart for the acquisition of ByteDance's US operations as TikTok's "trusted tech partner". While many in the industry remain confused on why Oracle would acquire the social media giant's 100 million monthly active users in the US, Constellation has publicly declared three reasons over the past few months:

  1. Battle for key workloads in the cloud wars. As with the win for Zoom and 8X8's Jitsi product, Oracle has shown how the Oracle Cloud Infrastructure can scale to handle the toughest workloads for the public cloud. Video requires a stable and highly elastic cloud. Oracle's next gen cloud infrastructure powered Zoom's amazing growth with minimal glitches. This gives them another massive workload.
  2. Once in a lifetime opportunity to create an ad network. Oracle's assets in third party data via the Oracle Data Cloud and acquisitions such as Blue Kai allow them to enter the ad network business with 100M MAU's monthly active users and potential access to 700 million MAU's worldwide. Oracle's data cloud can access actionable audience data on more than 300 million users and has 30,000 data attributes for direct marketing initiatives. TikTok is the fastest growing social network and ad platform in the US and gives Oracle instant social media credibility.
  3. Tech diplomacy in the US vs China trade wars. Given the geopolitical wars between US and China, this acquisition puts Oracle in the good graces of both governments. China and their investors will breathe a sigh of relief that ByteDance's assets aren't lost. The US government gains a win with the assets in US hands for privacy and national security.

The Bottom Line: Oracle Could Enter A New Era As A Digital Giant

Oracle's skill in winning TikTok is a coup on the cloud front, the ad tech front, and in the geopolitical trade wards. With over 80% of the top 20 ad networks, portals, creatives optimizers, trading desks, and ad brokers leveraging data from Oracle Data Marketplace, TikTok builds on this lead. With Rob Tarkoff, head of Oracle CX at the helm, there is a huge opportunity to compete head on with Google, Facebook, and Amazon for the estimated $600 billion dollar digital ad market in 2024. Add a next generation public cloud infrastructure to the mix and a shrewd management team, Oracle is now entering a new era where digital giants win based on their ability to:

  1. attract massive user bases;
  2. create signal intelligence with rich data;
  3. achieve decision velocity via automation and artificial intelligence; and
  4. deliver digital monetization models

Oracle's success in running TikTok and a consumer business will require a different mindset. If this ends up as just a pure technology partnership with Oracle playing the privacy and data enforcer, then the deal will lose the ad tech appeal. However, this once in a lifetime opportunity is one that Oracle has been building towards over the past five years. If the deal goes beyond just a cloud deal, expect Oracle to emerge as a new digital giant via this acquisition and compete head on with Amazon, Facebook, Google, Microsoft, and Twitter for ad revenue.

Your POV

Ready for Oracle in the consumer world? Do you think TikTok will succeed or fail? Add your comments to the blog or reach me via email: R (at) ConstellationR (dot) com or R (at) SoftwareInsider (dot) org.

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