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Tangoe Acquires MOBI – Part I: Context for the Acquisition

Industries: Enterprise Mobility Management, Technology Expense Management

Key Stakeholders: CIO, CFO, Chief Digital Officer, Chief Technology Officer, Chief Mobility Officer, Mobility Directors and Managers, Procurement Directors and Managers, Accounting Directors and Managers

Why It Matters: Tangoe is the largest technology expense management vendor. By purchasing MOBI, Tangoe gains Managed Mobility expertise, a customer base with high customer satisfaction, and expertise in Robotic Process Automation to support enterprise mobility.

Top Takeaway: Tangoe continues to aggressively acquire market leaders both to increase market share and add Best-in-Breed capabilities, personnel, and technology to its technology management portfolio.

Note: This is part of a four-blog series exploring Tangoe’s acquisition of MOBI.
Part I: Context for the Acquisition
Part II: Why?
Part III: Enterprise Considerations
Part IV: Market Considerations and Conclusion

On December 5th, 2018, Tangoe announced the acquisition of MOBI, a leading managed mobility services organization based in Indianapolis, Indiana in the United States. With this acquisition, Tangoe increases its IT spend under management to over $40 billion, increasing its lead over other spend management vendors with multiple billions of dollars of enterprise technology under management including Flexera, Snow Software, Microsoft Azure Cost Management, CloudHealth by VMware, Calero, MDSL, Cass Information Systems, and Sakon. Continue reading Tangoe Acquires MOBI – Part I: Context for the Acquisition

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The View from KubeCon+CloudNativeCon – Containers and Kubernetes Become Enterprise Ready

In case there was any doubt about the direction containers and Kubernetes are going, KubeCon+CloudNativeCon 2018 in Seattle should have dispelled them. The path is clear – technology is maturing and keeps adding more features that make it conducive to mission-critical, enterprise applications. From the very first day, the talk was about service meshes and network functions, logging and traceability, and storage and serverless compute. These are couplets that define the next generation of management, visibility, and core capabilities of a modern distributed application. On top of that is emerging security projects such as SPIFFE & SPIRE, TUF, Falco, and Notary. Management, visibility, growth in core functionality, and security. All of these are critical to making container platforms enterprise ready.

“The future of containers and Kubernetes as the base of the new stack was on display at KubeCon+CloudNativeCon and it’s a bright one.

Tom Petrocelli, Research Fellow, Amalgam Insights”

If the scope of KubeCon+CloudNativeCon and the Cloud Native Computing Foundation (CNCF) is any indication, the ecosystem is also growing. This year there were 8000 people at the conference – a sellout. The CNCF has grown to 300+ vendor members there are 46,000 contributors to its projects. That’s a lot of growth compared to just a few years ago. This many people don’t flock to sinking projects. Continue reading The View from KubeCon+CloudNativeCon – Containers and Kubernetes Become Enterprise Ready

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Amazon Expands Toolkit of Machine Learning Services at AWS re:Invent

At AWS re:Invent, Amazon Web Services expanded its toolkit of machine learning application services with the announcements of Amazon Comprehend Medical, Amazon Forecast, Amazon Personalize, and Amazon Textract. These new services augment the capabilities Amazon provides to end users when it comes to text analysis, personalized recommendations, and time series forecasts. The continued growth of these individual services removes obstacles for companies looking to get started with common machine learning tasks on a smaller scale; rather than building a wholesale data science pipeline in-house, these services allow companies to quickly get one task done, and this permits an incremental introduction to machine learning for a given organization. Forecast, Personalize, and Textract are in preview, while Comprehend Medical is available now.

Amazon Comprehend Medical, Forecast, Personalize, and Textract join a collection of machine learning services that include speech recognition (Transcribe) and translation (Translate), speech-to-text and text-to-speech (Lex and Polly) to power machine conversation such as chatbots and Alexa, general text analytics (Comprehend), and image and video analysis (Rekognition).

New Capabilities

Amazon Personalize lets developers add personalized recommendations into their apps, based on a given activity stream from that app and a corpus of what’s available to be recommended, whether that’s products, articles, or other things. In addition to recommendations, Personalize can also be used to customize search results and notifications. By combining a given search string or location with contextual behavior data, Amazon looks to provide customers with the ability to build trust.

Amazon Forecast builds private, custom time-series forecast models that predict future trends based on that data. Customers provide both histoical data and related causal data, and Forecast analyzes the data to determine the relevant factors in building its models and providing forecasts.

Amazon Textract extracts text and data from scanned documents, without requiring manual data entry or custom code. In particular, using machine learning to recognize when data is in a table or form field and treat it appropriately will save a significant amount of time over the current OCR standard.

Finally, Amazon Comprehend Medical, an extension of last year’s Amazon Comprehend, uses natural language processing to analyze unstructured medical text such as doctor’s notes or clinical trial records, and extract relevant information from this text.

Recommendations

Organizations doing resource planning, financial planning, or other similar forecasting that currently lack the capability to do time series forecasting in-house should consider using Amazon Forecast to predict product demand, staffing levels, inventory levels, material availability, and to perform financial forecasting. Outsourcing the need to build complex forecasting models in-house lets departments focus on the predictions.

Consumer-oriented organizations looking to build higher levels of engagement with their customers who provide generic, uncontextualized recommendations right now (based on popularity or other simple measures) should consider using Amazon Personalize to provide personalized recommendations, search results, and notifications via their apps and website. Providing high-quality relevant recommendations a la minute builds customer trust in the quality of a given organization’s engagement efforts, particularly compared to the average spray-and-pray marketing communication.

Organizations that still depend on physical documents, or who have an archive of physical documents to scan and analyze, should consider using Amazon Textract. OCR’s limits are well-known, especially when it comes to accurately interpreting and formatting semi-structured blocks of text data such as form fields and tables, resulting in significant time devoted to post-processing manual correction. Textract handles complex documents without the need for custom code or maintaining templates; being able to automate text interpretation and analysis further accelerates document processing workflows, and better permits organizations to maintain compliance.

Medical organizations using software that depends on manually-implemented rules to process their medical text should consider using Amazon Comprehend Medical. By removing the need to maintain a list of rules in-house, Comprehend Medical accelerates the ability to extract and analyze medical information from unstructured text fields like doctor’s notes and health records, improving processes such as medical coding, cohort analysis to recruit patients for clinical trials, and health monitoring of patients.

All organizations looking to use machine learning services from external providers need to consider whether outsourcing will work for their circumstances. Data privacy is a key concern, and even more so in regulated verticals with industry-specific rules such as HIPAA. Does the service you want to use respect those rules? From a compliance perspective, why a model gives the results it does needs to be explained as well; merely accepting results from the black box at face value is insufficient. Machine learning products that automatically provide such an explanation in plain English do exist, but this feature is still uncommon and in its infancy.

Conclusion

With its latest announcements, Amazon continues to broaden the scope of customer issues it addresses with machine learning services. Medical companies need better text analytics yesterday, but struggle to comply with HIPAA while assessing the data they have. Customer-facing organizations face stiff competition when their competitor is only a click away. And any company trying to plan for the future based on past data grapples with understanding what factors affect future results. Amazon’s machine learning application services address common tactical business issues by simplifying the process for customers of implementing task-specific machine learning models to pure inputs and outputs. These services present outsourcing opportunities for overworked departments struggling to keep up.

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Tangoe Acquires MOBI to Strategically Expand Enterprise Mobility Capabilities

On December 5th, 2018, Tangoe announced the acquisition of MOBI, a leading managed mobility services organization based in Indianapolis, Indiana in the United States. With this acquisition, Tangoe increases its IT spend under management to over $40 billion, increasing its lead over other spend management vendors with multiple billions of dollars of enterprise technology under management including Flexera, Snow Software, Microsoft Azure Cost Management, CloudHealth by VMware, Calero, MDSL, Cass Information Systems, and Sakon.

Key questions to consider for this acquisition include:

  • Why did Tangoe decide to buy MOBI at this time? For its customer base? Corporate culture? Technology?
  • How does this acquisition affect enterprises seeking toolsets to assist with the orchestration and accounting of digital transformation initiatives?
  • How will work be split and coordinated between Tangoe’s Austin logistics warehouse and MOBI’s Indianapolis-based facilities?
  • What will Tangoe do with MOBI’s Robotic Process Automation initiative of Mobots?
  • Will Tangoe keep MOBI’s staff or will there be a bunch of high-quality mobility and support staff available?
  • What happens to MOBI partners who may compete with Tangoe?
  • Will MOBI customers be moved to the Tangoe Matrix platform immediately?
  • Will Tangoe contribute to the burgeoning Indianapolis tech scene that is currently one of the hottest startup spots in the country?

To learn more about which of these questions can be answered and which of these questions require greater due diligence, please read my full analysis, which is available at: https://www.amalgaminsights.com/product/amalgam-insights-market-milestone-tangoe-acquires-mobi-to-enhance-mobility-management-capabilities

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Market Milestone – Looker Raises $103 Million E Round to Expand the Looker Data Platform

On December 6, 2018, Looker announced that it closed a $103 million E round led by Premji Invest, a private equity firm owned by Wipro chairman Azim Premji. This round also includes new funds from Cross Creek Advisors, a venture capital firm focused on late-stage investments with current investments including tech darlings such as Anaplan, Datastax, Docker, DocuSign, Pluralsight, and Sumo Logic. This round also included participation from current investors from prior rounds (such as Looker’s Series D covered by Amalgam Insights).

This round is expected to be a final round before a potential Initial Public Offering. Of course, in recent times, plans for IPO from hot companies have been interrupted at the last moment, such as with Workday’s $1.55 billion acquisition of Adaptive Insights (see Amalgam Insights’ coverage for more information) and SAP’s $8 billion acquisition of Qualtrics. A Looker IPO may not be guaranteed, but Amalgam Insights would expect that the valuation of Looker is likely not going to be affected whether the company ends up going public or being acquired for strategic reasons.

Why is Looker worth $1.6 billion?

With this funding, Looker crosses into “unicorn” territory with a valuation of approximately $1.6 billion. This valuation is based on Looker’s run-rate of over $100 million in annual revenue supported by an employee base that is approaching 600, year-over-year growth exceeding 70%, and expansion into Tokyo to support the Asia-Pacific region.

Amalgam Insights believes that this valuation reflects several key trends and intelligent strategic decisions made by Looker in supporting enterprise-grade business intelligence and data supply chain needs.

First, Looker was developed to support a variety of data preparation, cataloging, governance, querying, and presentation capabilities at scale across a wide variety of data sources and formats. This approach reflects Looker’s purpose of being a data platform built for a new generation of data analysts based on the volume and variety of challenges that drive current analytic challenges.

Taking a step back, I remember when I first ran into Looker on the trade show circuit. I was looking at a variety of BI solutions in 2013, including the likes of Tableau, Qlik, and Microstrategy, when I ran into a small booth manned by a guy named Keenan Rice, who currently serves as Looker’s VP of Strategic Alliances. As a jaded analyst, I asked how his solution was different from the 30+ other solutions that were being shown on that show floor. At the time, everyone was bragging about their pixel-perfect visualizations, report building capabilities, and basic data presentation that were interesting to see, but rarely provided significant value, competitive differentiation, or Return on Investment.

However, Keenan’s pitch differed substantially. Although Looker also provided visualizations for large data tables, Keenan started by talking about data analyst challenges in preparing data for self-service analytics, using Looker’s SQL-based LookML as a modelling layer to access a variety of data, bringing new data into existing data and analytic workflows, and delivering it all as Software-as-a-Service. As a former data analyst, this was all music to my ears, but I wondered if Looker would be able to stand out as a solution against all of the dashboarding solutions, report builders, and the massive marketing budgets of incumbent BI vendors. Looker was just starting as a company and had just announced its Series A, so it faced significant odds in standing out based on the $10 million to $40 million range of Series A or Series B funding that a company like Looker would typically get at this point.

But it has been a pleasure both to seek Looker grow over the years and to lead a new generation of solutions focused on simplifying data supply chains and pipelines. It ends up that Looker was just early enough to avoid having comparable competition while solving a market need that businesses understood, especially those companies seeking a new generation of BI-based capabilities and wanting to develop a more data-driven organization. So, that’s a long way of saying that Looker took a big and laborious bet against the grain six years ago and has been rewarded for having a combination of good product and good timing.

But this isn’t the only reason that Looker has been successful.

As Looker has achieved market fit and success, the solution has evolved from a data workflow solution into an emerging application development solution that allows analysts and developers to work collaboratively in building secure and embeddable data models. By truly building Looker as a platform rather than simply calling it a platform as an aspirational goal based on a set of APIs, Amalgam Insights expects that Looker will become increasingly valuable for data analysts and the “citizen” developers who seek to increase appropriate access to enterprise data and analytic outputs.

And Looker has now taken an important step forward in providing department-specific apps in the most recent Looker 6 launch. Starting with digital marketing and web analytics, Looker is now taking advantage of its analytics capabilities to provide out-of-the-box support to help enterprises with key challenges rather than forcing clients to build foundational analytics that have been built over and over. Both these apps and Looker’s development focus build on top of Looker’s prior focus on Looker Blocks first launched in 2015, which were a collection of SQL, visualizations, and pre-built analytic models designed to accelerate analytic projects.

So, what is next for Looker? IPO? Global domination? 

All kidding aside, with expansion into AsiaPac to accompany Looker’s existing European offices in London and Dublin and Looker’s current 70% growth rate, it is possible that Looker could increase net-new revenues in 2019 faster than BI stalwarts such as Information Builders and Qlik. From a financial perspective, Amalgam Insights notes that Looker’s growth mirrors that of Alteryx, which has roughly quadrupled its stock price since its March 2017 IPO which was based on Alteryx’ 2016 revenue of roughly $86 million with 59% year-over-year growth.

Looker has stated that it believes that this Series E round should be its last funding round before IPO and, given recent market valuations for successful software companies, there should be no reason to expect otherwise from Looker. Looker’s progress both as a software development platform as well as a platform of pre-built applications and services bodes well for Looker as it continues to evolve as an enterprise platform focused on expanding access to data insights.

Finally, Amalgam Insights believes that Looker’s success will lead to continued success with other data and analytics companies focusing on rapid data modelling, data mapping, and analysis of multiple data sources. Looker’s agile BI approach that avoids the challenges of traditional BI and ETL solutions in supporting multiple data sources has become a new standard for accelerating the value of data. This both means that traditional BI companies will need to accelerate their own data pipeline efforts or to partner with other vendors and that Looker will start to be targeted in the same way that the likes of Tableau, Qlik, and Microstrategy have in the past. The price of success is increased competition and innovation, which is good news for the BI, data, and analytics markets and should provide Looker with enough challenges to avoid resting on its laurels.

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Tom Petrocelli Publishes Groundbreaking Report Defining Serverless Computing

On December 10th, 2018, Tom Petrocelli published the Market Guide for Serverless Computing entitled “Serverless Computing Provides New Solutions to Modern Problems” in conjunction with KubeCon + CloudNativeCon North America 2018.

This report was written in response to massive market confusion regarding the current definition of serverless computing and the categories of options that software, platform, and infrastructure architects can use to initiate serverless computing projects.

“Serverless can best be thought of as any computer system that abstracts the infrastructure for the developer, employs an event-driven model, and only consumes resources when needed.”

Tom Petrocelli, Research Fellow, Amalgam Insights

In this report, Petrocelli provides a definition of serverless computing, provides five key use cases for serverless computing, explores the economics of serverless computing, and provides 14 representative enterprise-grade solutions across open source projects, cloud services, and on-premises commercial products.

Amalgam Insights’ Market Guides provide an unbiased, third-party perspective for explaining new technology and service capabilities based on our decades of expert experience, briefings with leading technology vendors, and discussions with Early Adopter organizations.

To download the report, which will be available at no cost throughout the duration of KubeCon + CloudNativeCon North America 2018, please download at the following link: https://www.amalgaminsights.com/product/market-guide-serverless-computing-provides-new-solutions-to-modern-problems

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Data Science and Machine Learning News, November 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, SnapLogic, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Continue reading Data Science and Machine Learning News, November 2018

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Red Hat Hybrid Cloud Management Gets Financial with Cloud Cost Management

Key Stakeholders: CIO, CFO, Accounting Directors and Managers, Procurement Directors and Managers, Telecom Expense Personnel, IT Asset Management Personnel, Cloud Service Managers, Enterprise Architects

Why It Matters: As enterprise cloud infrastructure continues to grown 30-40% per year and containerization becomes a top enterprise concern, IT must have tools and a strategy for managing the cost of storage and compute associated with both hybrid cloud and container spend. With Cloud Cost Management, Red Hat provides an option for its considerable customer base.

Key Takeaways: Red Hat OpenShift customers seeking to managing the computing costs associated with hybrid cloud and containers should starting trialing Cloud Cost Management when it becomes available in 2019. Effective cost management strategies and tools should be considered table stakes for all enterprise-grade technologies.

Amalgam Insights is a top analyst firm in the analysis of IT subscription cost management, as can be seen in our:

In this context, Red Hat’s intended development of multi-cloud cost management integrated with CloudForms is an exciting announcement for the cloud market. This product, scheduled to come out in early 2019, will allow enterprises supporting multiple cloud vendors to support workload-specific cost management, which Amalgam Insights considers to be a significant advancement in the cloud cost management market.

And this product comes at a time when cloud infrastructure cost management has seen significant investment including VMware’s $500 million purchase of Boston-based CloudHealth Technologies, the 2017 $50 million “Series A” investment in CloudCheckr, investments in this area by leading Telecom and Technology Expense Management vendors such as Tangoe and Calero, and recent acquisitions and launches in this area from the likes of Apptio, BMC, Microsoft, HPE, and Nutanix.

However, the vast majority of these tools are currently lacking in the granular management of cloud workloads that can be tracked at a service level and then appropriately cross-charged to a project, department, or location. This capability will be increasingly important as application workloads become increasingly nuanced and revenue-driven accounting of IT becomes increasingly important. Amalgam Insights believes that, despite the significant activity in cloud cost management, that this market is just starting to reach a basic level of maturity as enterprises continue to increase their cloud infrastructure spend by 40% per year or more and start using multiple cloud vendors to deal with a variety of storage, computing, machine learning, application, service, integration, and hybrid infrastructure needs.

Red Hat Screenshot of Hybrid Cloud Cost Management

As can be seen from the screenshot, Red Hat’s intended Hybrid Cloud Cost Management offering reflects both modern design and support for both cloud spend and container spend. Given the enterprise demand for third-party and hybrid cloud cost management solutions, it makes sense to have an OpenShift-focused cost management solution.

Amalgam Insights has constantly promoted the importance of formalized technology cost management initiatives and their ability in reducing IT cost categories by 30% or more. We believe that Red Hat’s foray into Hybrid Cloud Cost Management has an opportunity to compete with a crowded field of competitors in managing multi-cloud and hybrid cloud spend. Despite the competitive landscape already in play, Red Hat’s focus on the OpenShift platform as a starting point for cost management will be valuable for understanding cloud spend at container, workload, and microservices levels that are currently poorly understood by IT executives.

My colleague Tom Petrocelli has noted that “I would expect to see more and more development shift to open source until it is the dominant way to develop large scale infrastructure software.” As this shift takes place, the need to manage the financial and operational accounting of these large-scale projects will become a significant IT challenge. Red Hat is demonstrating its awareness of this challenge and has created a solution that should be considered by enterprises that are embracing both Open Source and the cloud as the foundations for their future IT development.

Recommendations

Companies already using OpenShift should look forward to trialling Cloud Cost Management when it comes out in early 2019. This product provides an opportunity to effectively track the storage and compute costs of OpenShift workloads across all relevant infrastructure. As hybrid and multi-cloud management becomes increasingly common, IT organizations will need a centralized capability to track their increasingly complex usage associated with the OpenShift Container Platform.

Cloud Service Management and Technology Expense Management solutions focused on tracking Infrastructure as a Service spend should consider integration with Red Hat’s Cloud Cost Management solution. Rather than rebuild the wheel, these vendors can take advantage of the work already done by RedHat to track container spend.

And for Red Hat, Amalgam Insights provides the suggestion that Cloud Cost Management become more integrated with CloudForms over time. The most effective expense management practices for complex IT spend categories always include a combination of contracts, inventory, invoices, usage, service orders, service commitments, vendor comparisons, and technology category comparisons. To gain this holistic view that optmizes infrastructure expenses, cloud procurement and expense specialists will increasingly demand this complete view across the entire lifecycle of services.

Although this Cloud Cost Management capability has room to grow, Amalgam Insights expects this tool to quickly become a mainstay, either as a standalone tool or as integrated inputs within an enterprise’s technology expense or cloud service management solution. As with all things Red Hat, Amalgam Insights expects rapid initial adoption within the Red Hat community in 2019-2020 which will drive down enterprise infrastructure total cost of ownership and increase visibility for enterprise architects, financial controllers, and accounting managers responsible for responsible IT cost management.

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Observations on the Future of Red Hat from Red Hat Analyst Day

On November 8th, 2018, Amalgam Insights analysts Tom Petrocelli and Hyoun Park attended the Red Hat Analyst Day in Boston, MA. We had the opportunity to visit Red Hat’s Boston office in the rapidly-growing Innovation District, which has become a key tech center for enterprise technology companies. In attending this event, my goal was to learn more about the Red Hat culture that is being acquired as well as to see how Red Hat was taking on the challenges of multi-cloud management.

Throughout Red Hat’s presentations throughout the day, there was a constant theme of effective cross-selling, growing deal sizes including a record 73 deals of over $1 million in the last quarter, over 600 accounts with over $1 million in business in the last year, and increased wallet share year-over-year for top clients with 24 out of 25 of the largest clients increasing spend by an average of 15%. The current health of Red Hat is undeniable, regardless of the foibles of the public market. And the consistency of Red Hat’s focus on Open Source was undeniable across infrastructure, integration, application development, IT automation, IT optimization, and partner solutions, which demonstrated how synchronized and focused the entire Red Hat executive team presenters were, including Continue reading Observations on the Future of Red Hat from Red Hat Analyst Day

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Torchbearer Case Study: OceanX Delivers the Consumer Subscription Experience with Oracle Cloud Infrastructure

(Note: Torchbearer Case Studies provide enterprises with a strong example of the “Art of the Possible” in using technology to enhance their business environment. Amalgam Insights provides emerging best practices based on the experience of the Torchbearer to inspire and educate Early Adopter and Early Majority buyers seeking to develop a strategic advantage.)

At Oracle Open World, I was interested in learning more about the Oracle Cloud and its role in data and analytics. Although Oracle has admittedly been relatively late to the enterprise cloud, the Oracle Cloud was front and center throughout Oracle Open World and it was interesting to see how Oracle was able to bring up enterprise technology deployments across its portfolio. One example of enterprise success in working with the Oracle Cloud that stood out was from OceanX.

At Oracle Open World, OceanX won the 2018 Oracle Innovation Award for Data Management. OceanX is a spinoff of Guthy-Renker started in 2016 to provide an integrated subscription commerce platform which brings together e-commerce, fulfilment, customer service, and business analytics associated with direct-to-consumer subscription programs. This allows consumer brands to provide personalized and bespoke products in their subscription offerings and to build consumer relationships based on a holistic view of the customer’s preferences, purchases, and interactions.
Continue reading Torchbearer Case Study: OceanX Delivers the Consumer Subscription Experience with Oracle Cloud Infrastructure