Market Milestone – Google to Buy Looker to Transform Business Analytics

Key Stakeholders:

Chief Information Officers, Chief Technical Officers, Chief Digital Officers, Chief Analytics Officers, Data Monetization Directors and Managers, Analytics Directors and Managers, Data Management Directors and Managers, Enterprise Architects

Why It Matters:

Google’s proposed $2.6 billion acquisition of Looker provides Google with a core data engagement, service, and application environment to support Google Cloud Platform. This represents an impressive exit for Looker, which was expected to IPO after its December 2018 Series E round. This report covers key considerations for Looker customers, GCP customers, and enterprises seeking to manage data and analytics in a multi-cloud or hybrid cloud environment.

Top Takeaway:

Google Cloud Platform intends to acquire a Best-in-Breed platform for cloud analytics, embedded BI, and native analytic applications in Looker. By filling this need for Google customers, GCP has strengthened its positioning for enterprise cloud customers at a time when Amalgam Insights expects rapid and substantial growth of 25%+ CAGR (Compound Annual Growth Rate) across cloud markets for the next few years. This acquisition will help Google to remain a significant and substantial player as an enterprise cloud provider and demonstrates the latitude that Google Cloud CEO Thomas Kurian has in acquiring key components to position GCP for future growth.

To read the rest of this piece, please visit Looker, which has acquired a commercial license for this research.

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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://amalgaminsights.com/product/market-guide-serverless-computing-provides-new-solutions-to-modern-problems

EPM at a Crossroads: Big Data Solutions

Key Stakeholders: Chief Information Officers, Chief Financial Officers, Chief Operating Officers, Chief Digital Officers, Chief Technology Officer, Accounting Directors and Managers, Sales Operations Directors and Managers, Controllers, Finance Directors and Managers, Corporate Planning Directors and Managers

Analyst-Recommended Solutions: Adaptive Insights, a Workday Company, Anaplan, Board, Domo, IBM Planning Analytics, OneStream, Oracle Planning and Budgeting, SAP Analytics Cloud

In 2018, the Enterprise Performance Management market is at a crossroads. This market has emerged from a foundation of financial planning, budgeting, and forecasting solutions designed to support basic planning and has evolved as the demands for business planning, risk and forecasting management, and consolidation have increased over time. In addition, the EPM market has expanded as companies from the financial consolidation and close markets, business performance management markets, and workflow and process automation markets now play important roles in effectively managing Enterprise Performance.

In light of these challenges, Amalgam Insights is tracking six key areas where Enterprise Performance Management is fundamentally changing: Big Data, Robotic Process Automation, API connectivity, Analytics and Data Science, Vertical Solutions, and Design Thinking for User Experience

Supporting Big Data for Enterprise Performance Management

Amalgam Insights has identified two key drivers repeatedly mentioned by finance departments seeking to support Big Data in Enterprise Performance Management. First, EPM solutions must support larger stores of data over time to fully analyze financial data and a plethora of additional business data needed to support strategic business analysis. The challenge of growing data has become increasingly important as enterprises now face the challenge of managing billion row tables and outgrow the traditional cubes and datamarts used to manage basic financial data. The sheer scale of financial and commerce-related transactional data requires a Big Data approach at the enterprise level to support timely analysis of planning, consolidation, close, risk, and compliance.

In addition, these large data sources need to integrate with other data sources and references to support integrated business planning to align finance planning with sales, supply chain, IT, and other departments. As the CFO is increasingly asked to be not only a financial leader, but a strategic leader, she must have access to all relevant business drivers and have a single view of how relevant sales, support, supply chain, marketing, operational, and third-party data are aligned to financial performance. Each of these departments has its own large store of data that the strategic CFO must also be able to access, allocate, and analyze to guide the business.

New EPM solutions must evolve beyond traditional OLAP cubes to support hybrid data structures that effectively scale to support the immense scale and variety of data being supported. Amalgam notes that EPM solutions focusing on large data solutions take a variety of relational, in-memory, columnar, cloud computing, and algorithmic approaches to define categories on the fly, store, structure, and analyze financial data.

To support these large stores of data and effectively support them from a financial, strategic, and analytic perspective, Amalgam Insights recommends the following companies that have been innovative in supporting immense and varied planning and budgeting data environments based on briefings and discussions held in 2018:

  • Adaptive Insights, a Workday Company
  • Anaplan
  • Board
  • Domo
  • IBM Planning Analytics
  • OneStream
  • Oracle Planning and Budgeting
  • SAP Analytics Cloud

Adaptive Insights

Adaptive Insights’ Elastic Hypercube, an in-memory, dynamic caching and scaling solution announced in July 2018. Amalgam Insights saw a preview of this technology at Adaptive Live and was intrigued by the efficiency that Adaptive Insights provided to models in selectively recalculating only the dependent changes as a model was edited, using a dynamic caching approach for only using memory and computational cycles when data was being accessed, and using both tabular and cube formats to support data structures. This data format will also be useful to Adaptive Insights as a Workday company in building out the various departmental planning solutions that will be accretive to Workday’s positioning as an HR and ERP solution after Workday’s June acquisition (covered in June in our Market Milestone).

Anaplan

Anaplan’s Hyperblock is an in-memory engine combining columnar, relational, and OLAP approaches. This technology is the basis of Anaplan’s platform and allows Anaplan to rapidly support large planning use cases. By developing composite dimensions, Anaplan users can pre-build a broad array of combinations that can be used to repeatably deploy analytic outputs. As noted in our March blog, Anaplan has been growing rapidly based on its ability to rapidly support new use cases. In addition, Anaplan has recently filed its S-1 to go public.

Board

Board goes to market both as an EPM and a general business intelligence solution. Its core technology is the Hybrid Bitwise Memory Pattern (HBMP), a proprietary in-memory data management solution, designed to algorithmically map each bit of data, then to store this map in-memory. In practice, this approach allows Board to allow many users to both access and edit information without dealing with lagging or processing delays. This approach also allows Board to support which aspects of data to support in an in-memory or dynamic manner to prioritize computing assets.

Domo

Domo describes its Adrenaline engine as an “n-dimensional, highly concurrent, exo-scale, massively parallel, and sub-second data warehouse engine” to store business data. This is accompanied by VAULT, Domo’s data lake to support data ingestion and serve as a single store of record for business analysis. Amalgam Insights covered the Adrenaline engine as one of Domo’s “Seven Samurai” in our March report Domo Hajimemashite: At Domopalooza 2018, Domo Solves Its Case of Mistaken Identity. Behind the buzzwords, these technologies allow Domo to provide executive reporting capabilities across a wide range of departmental use cases in near-real time. Although Domo is not a budgeting solution, it is focused on portraying enterprise performance for executive consumption and should be considered for organizations seeking to gain business-wide visibility to key performance metrics.

IBM Planning Analytics

IBM Planning Analytics runs on Cognos TM1 OLAP in-memory cubes. To increase performance, these cubes use sparse memory management where missing values are ignored and empty values are not stored. In conjunction with IBM’s approach of caching analytic outcomes in-memory, this approach allows IBM to improve performance compared to standard OLAP approaches and this approach has been validated at scale by a variety of IBM Planning Analytics clients. Amalgam Insights presented on the value of IBM’s approach at IBM Vision 2017 both from a data perspective and from a user interface perspective that will be covered in a future blog.

OneStream

OneStream provides in-memory processing & stateless servers to support scale, but their approach to analytic scale is based on virtual cubes and extensible dimensions, which allow organizations to continue building dimensions over time that are tied back to a corporate level and to create logical views of data based on a larger data store to support specific financial tasks such as budgeting, tax reporting, or financial reporting. OneStream’s approach is focused on financial use rather than general business planning.

Oracle Planning and Budgeting Cloud

Oracle Planning and Budgeting Cloud Service is based on Oracle Hyperion, the market leader in Enterprise Performance Management from a revenue perspective. The Oracle Cloud is built on Oracle Exalogic Elastic Cloud, Oracle Exadata Database Machine, and the Oracle Database, which provide a strong in-memory foundation for the Planning and Budgeting application by providing an algorithmic approach to manage storage, compute, and networking. This approach effectively allows Oracle to support planning models at massive scale.

SAP Analytics Cloud

SAP Analytics Cloud, SAP’s umbrella product for planning and business intelligence, uses SAP S/4HANA, an in-memory columnar relational database, to provide real-time access to data and to accelerate both modelling and analytic outputs based on all relevant transactional data. This approach is part of SAP’s broader HANA strategy to encapsulate both analytic and transactional processing in a single database, effectively making all data reportable, modellable, and actionable. SAP has also recently partnered with Intel Optane DC persistent memory to support larger data volumes for enterprises requiring larger persistent data stores for analytic use.

This blog is part of a multi-part series on the evolution of Enterprise Performance Management and key themes that the CFO office must consider in managing holistic enterprise performance: Big Data, Robotic Process Automation, API connectivity, Analytics and Data Science, Vertical Solutions, and Design Thinking for User Experience. If you would like to set up an inquiry to discuss EPM or provide a vendor briefing on this topic, please contact us at info@amalgaminsights.com to set up time to speak.

Last Blog: EPM at a Crossroads
Next Blog: Robotic Process Automation and Machine Learning in EPM

The “Unlearning” Dilemma in Learning and Development

Key Stakeholders: IT Managers, IT Directors, Chief Information Officers, Chief Technology Officers, Chief Digital Officers, IT Governance Managers, and IT Project and Portfolio Managers. Top Takeaways: One critical barrier to full adoption is the poorly addressed problem of unlearning. Anytime a new piece of software achieves some goal with a set of motor behaviors that…

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VMware Purchases CloudHealth Technologies to support Multicloud Enterprises and Continue Investing in Boston


Vendors and Solutions Mentioned: VMware, CloudHealth Technologies, Cloudyn, Microsoft Azure Cloud Cost Management, Cloud Cruiser, HPE OneSphere. Nutanix Beam, Minjar, Botmetric

Key Stakeholders: Chief Financial Officers, Chief Information Officers, Chief Accounting Officers, Chief Procurement Officers, Cloud Computing Directors and Managers, IT Procurement Directors and Managers, IT Expense Directors and Managers

Key Takeaway: As Best-of-Breed vendors continue to emerge, new technologies are invented, existing services continue to evolve, vendors pursue new and innovative pricing and delivery models, cloud computing remains easy to procure, and IaaS doubles every three years as a spend category, cloud computing management will only increase in complexity and the need for Cloud Service Management will only increase. VMware has made a wise choice in buying into a rapidly growing market and now has greater opportunity to support and augment complex peak, decentralized, and hybrid IT environments.

About the Announcement

On August 27, 2018, VMware announced a definitive agreement to acquire CloudHealth Technologies, a Boston-based startup company focused on providing a cloud operations and expense management platform that supports enterprise accounts across Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

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Workday Surprises the IPO Market and Acquires Adaptive Insights

Key Stakeholders: Chief Information Officers, Chief Financial Officers, Chief Operating Officers, Chief Digital Officers, Chief Technology Officer, Accounting Directors and Managers, Sales Operations Directors and Managers, Controllers, Finance Directors and Managers, Corporate Planning Directors and Managers Why It Matters: Workday snatched Adaptive Insights away from the public markets only days before IPO, acquiring a proven enterprise planning…

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Why Oktane Has Become The Most Important SaaS Event of the Year

Key Stakeholders: Chief Information Officers, Chief Digital Officers, Chief Information Security Officers, Security Directors and Managers , Security Operations Directors, IT Architects, IT Strategists, Identity and Access Directors and Managers, Software Engineers, Cloud Strategists

Why This Matters: Cloud and digital strategists who are not attending Oktane risk missing out on key SaaS and IT management strategies emerging in an end-user centric model of IT.

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Revealing the Learning Science for Improving IT Onboarding

Key Stakeholders: IT Managers, IT Directors, Chief Information Officers, Chief Technology Officers, Chief Digital Officers, IT Governance Managers, and IT Project and Portfolio Managers. Top Takeaways: Information technology is innovating at an amazing pace. These technologies hold the promise of increased effectiveness, efficiency and profits. Unfortunately, the training tools developed to onboard users are often…

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